Systematic review of observational studies of the impact of cardiovascular risk factors on preeclampsia in sub-saharan Africa
Bibliographic record
Abstract
BACKGROUND: Maternal mortality is a public health issue, particularly in low- and middle-income countries (LMIC). Sub-Saharan Africa (SSA) is the region most affected worldwide by maternal mortality, and preeclampsia is one of the main causes. We performed a systematic review of observational studies to identify the impact of cardiovascular risk factors on preeclampsia in SSA with a more representative sample. METHODS: Databases: PubMed and Google Scholar were searched to identify published studies. Studies were included if they reported results on the link between at least one cardiovascular risk factor and preeclampsia. Relevant studies quality was assessed with the Newcastle-Ottawa Scale (NOS). Odds ratios and relative risk (RR) were reported with their confidence intervals. RESULTS: Twelve articles (8 case-controls, 3 cohorts, 1 cross-sectional) were included in this review, with a total of 24,369 pregnant women. Cardiovascular risk factors such as chronic hypertension, overweight, obesity, diabetes and alcohol were significantly associated with a high risk of preeclampsia. Very few data were available concerning some risk factors. None of the articles reported tobacco consumption as a preeclampsia risk factor. There is a lack of data from French-speaking SSA countries. CONCLUSION: Cardiovascular risk factors increase the risk of preeclampsia. Our results suggest the need for prospective cohort studies to ascertain this association in order to reduce maternal mortality due to preeclampsia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".